PVC Recognition for Wearable ECGs Using Modified Frequency Slice Wavelet Transform and Convolutional Neural Network

Abstract

Progress in wearable techniques makes the long-term daily electrocardiogram (ECG) monitoring possible. Premature ventricular contraction (PVC) is one of the most common cardiac arrhythmias. This study proposed a method by combining the modified frequency slice wavelet transform (MFSWT) and convolutional neural network (CNN). Training data are from the 2018 China physiological signal challenge (934 PVC and 906 non-PVC recordings). The first 10-s ECG waveforms in each recording were transformed into 2-D time-frequency images (frequency range of 0-50 Hz and size of 300 × 100) using MFSWT. A 25-layer CNN structure was constructed, which includes five convolution layers with kernel size of 3×3, five dropout layers, five ReLU layers, five maximum pooling layers with kernel size of 2 × 2, a flatten layer, two fully connected layers, as well as the input and output layers. Test data were recorded from 12-lead Smart ECG vests, including 775 PVC and 742 non-PVC recordings. Results showed that, the proposed method achieved a high accuracy of 97.89% for PVC/non-PVC episodes classification, indicating that the combination of MFSWT and CNN provides new insight to accurately identify PVC from the wearable ECG recordings.